What does the term 'feature importance' refer to in the context of Random Forests?
Practice Questions
1 question
Q1
What does the term 'feature importance' refer to in the context of Random Forests?
The number of features used in the model
The contribution of each feature to the model's predictions
The correlation between features
The total number of trees in the forest
Feature importance indicates how much each feature contributes to the model's predictions, helping to identify the most influential variables.
Questions & Step-by-step Solutions
1 item
Q
Q: What does the term 'feature importance' refer to in the context of Random Forests?
Solution: Feature importance indicates how much each feature contributes to the model's predictions, helping to identify the most influential variables.
Steps: 6
Step 1: Understand that a 'feature' is a piece of information used to make predictions in a model.
Step 2: Recognize that 'feature importance' measures how much each feature helps the model make accurate predictions.
Step 3: In Random Forests, the model uses many decision trees to make predictions.
Step 4: Each decision tree looks at different features to make decisions.
Step 5: Feature importance is calculated by seeing how much each feature improves the predictions across all the trees.
Step 6: Features that improve predictions a lot are considered more important than those that do not.
